Machine Learning Theory and Applications : Hands-On Use Cases with Python on Classical and Quantum Machines

Machine Learning Theory and Applications Hands-On Use Cases with Python on Classical and Quantum Machines

Detalles Bibliográficos
Autor principal: Vasques, Xavier (-)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated 2024.
Edición:1st ed
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009811321006719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright Page
  • Dedication Page
  • Contents
  • Foreword
  • Acknowledgments
  • General Introduction
  • Chapter 1 Concepts, Libraries, and Essential Tools in Machine Learning and Deep Learning
  • 1.1 Learning Styles for Machine Learning
  • 1.1.1 Supervised Learning
  • 1.1.1.1 Overfitting and Underfitting
  • 1.1.1.2 K-Folds Cross-Validation
  • 1.1.1.3 Train/Test Split
  • 1.1.1.4 Confusion Matrix
  • 1.1.1.5 Loss Functions
  • 1.1.2 Unsupervised Learning
  • 1.1.3 Semi-Supervised Learning
  • 1.1.4 Reinforcement Learning
  • 1.2 Essential Python Tools for Machine Learning
  • 1.2.1 Data Manipulation with Python
  • 1.2.2 Python Machine Learning Libraries
  • 1.2.2.1 Scikit-learn
  • 1.2.2.2 TensorFlow
  • 1.2.2.3 Keras
  • 1.2.2.4 PyTorch
  • 1.2.3 Jupyter Notebook and JupyterLab
  • 1.3 HephAIstos for Running Machine Learning on CPUs, GPUs, and QPUs
  • 1.3.1 Installation
  • 1.3.2 HephAIstos Function
  • 1.4 Where to Find the Datasets and Code Examples
  • Further Reading
  • Chapter 2 Feature Engineering Techniques in Machine Learning
  • 2.1 Feature Rescaling: Structured Continuous Numeric Data
  • 2.1.1 Data Transformation
  • 2.1.1.1 StandardScaler
  • 2.1.1.2 MinMaxScaler
  • 2.1.1.3 MaxAbsScaler
  • 2.1.1.4 RobustScaler
  • 2.1.1.5 Normalizer: Unit Vector Normalization
  • 2.1.1.6 Other Options
  • 2.1.1.7 Transformation to Improve Normal Distribution
  • 2.1.1.8 Quantile Transformation
  • 2.1.2 Example: Rescaling Applied to an SVM Model
  • 2.2 Strategies to Work with Categorical (Discrete) Data
  • 2.2.1 Ordinal Encoding
  • 2.2.2 One-Hot Encoding
  • 2.2.3 Label Encoding
  • 2.2.4 Helmert Encoding
  • 2.2.5 Binary Encoding
  • 2.2.6 Frequency Encoding
  • 2.2.7 Mean Encoding
  • 2.2.8 Sum Encoding
  • 2.2.9 Weight of Evidence Encoding
  • 2.2.10 Probability Ratio Encoding
  • 2.2.11 Hashing Encoding
  • 2.2.12 Backward Difference Encoding.
  • 2.2.13 Leave-One-Out Encoding
  • 2.2.14 James-Stein Encoding
  • 2.2.15 M-Estimator Encoding
  • 2.2.16 Using HephAIstos to Encode Categorical Data
  • 2.3 Time-Related Features Engineering
  • 2.3.1 Date-Related Features
  • 2.3.2 Lag Variables
  • 2.3.3 Rolling Window Feature
  • 2.3.4 Expending Window Feature
  • 2.3.5 Understanding Time Series Data in Context
  • 2.4 Handling Missing Values in Machine Learning
  • 2.4.1 Row or Column Removal
  • 2.4.2 Statistical Imputation: Mean, Median, and Mode
  • 2.4.3 Linear Interpolation
  • 2.4.4 Multivariate Imputation by Chained Equation Imputation
  • 2.4.5 KNN Imputation
  • 2.5 Feature Extraction and Selection
  • 2.5.1 Feature Extraction
  • 2.5.1.1 Principal Component Analysis
  • 2.5.1.2 Independent Component Analysis
  • 2.5.1.3 Linear Discriminant Analysis
  • 2.5.1.4 Locally Linear Embedding
  • 2.5.1.5 The t-Distributed Stochastic Neighbor Embedding Technique
  • 2.5.1.6 More Manifold Learning Techniques
  • 2.5.1.7 Feature Extraction with HephAIstos
  • 2.5.2 Feature Selection
  • 2.5.2.1 Filter Methods
  • 2.5.2.2 Wrapper Methods
  • 2.5.2.3 Embedded Methods
  • 2.5.2.4 Feature Importance Using Graphics Processing Units (GPUs)
  • 2.5.2.5 Feature Selection Using HephAIstos
  • Further Reading
  • Chapter 3 Machine Learning Algorithms
  • 3.1 Linear Regression
  • 3.1.1 The Math
  • 3.1.2 Gradient Descent to Optimize the Cost Function
  • 3.1.3 Implementation of Linear Regression
  • 3.1.3.1 Univariate Linear Regression
  • 3.1.3.2 Multiple Linear Regression: Predicting Water Temperature
  • 3.2 Logistic Regression
  • 3.2.1 Binary Logistic Regression
  • 3.2.1.1 Cost Function
  • 3.2.1.2 Gradient Descent
  • 3.2.2 Multinomial Logistic Regression
  • 3.2.3 Multinomial Logistic Regression Applied to Fashion MNIST
  • 3.2.3.1 Logistic Regression with scikit-learn
  • 3.2.3.2 Logistic Regression with Keras on TensorFlow.
  • 3.2.4 Binary Logistic Regression with Keras on TensorFlow
  • 3.3 Support Vector Machine
  • 3.3.1 Linearly Separable Data
  • 3.3.2 Not Fully Linearly Separable Data
  • 3.3.3 Nonlinear SVMs
  • 3.3.4 SVMs for Regression
  • 3.3.5 Application of SVMs
  • 3.3.5.1 SVM Using scikit-learn for Classification
  • 3.3.5.2 SVM Using scikit-learn for Regression
  • 3.4 Artificial Neural Networks
  • 3.4.1 Multilayer Perceptron
  • 3.4.2 Estimation of the Parameters
  • 3.4.2.1 Loss Functions
  • 3.4.2.2 Backpropagation: Binary Classification
  • 3.4.2.3 Backpropagation: Multi-class Classification
  • 3.4.3 Convolutional Neural Networks
  • 3.4.4 Recurrent Neural Network
  • 3.4.5 Application of MLP Neural Networks
  • 3.4.6 Application of RNNs: LST Memory
  • 3.4.7 Building a CNN
  • 3.5 Many More Algorithms to Explore
  • 3.6 Unsupervised Machine Learning Algorithms
  • 3.6.1 Clustering
  • 3.6.1.1 K-means
  • 3.6.1.2 Mini-batch K-means
  • 3.6.1.3 Mean Shift
  • 3.6.1.4 Affinity Propagation
  • 3.6.1.5 Density-based Spatial Clustering of Applications with Noise
  • 3.7 Machine Learning Algorithms with HephAIstos
  • References
  • Further Reading
  • Chapter 4 Natural Language Processing
  • 4.1 Classifying Messages as Spam or Ham
  • 4.2 Sentiment Analysis
  • 4.3 Bidirectional Encoder Representations from Transformers
  • 4.4 BERT's Functionality
  • 4.5 Installing and Training BERT for Binary Text Classification Using TensorFlow
  • 4.6 Utilizing BERT for Text Summarization
  • 4.7 Utilizing BERT for Question Answering
  • Further Reading
  • Chapter 5 Machine Learning Algorithms in Quantum Computing
  • 5.1 Quantum Machine Learning
  • 5.2 Quantum Kernel Machine Learning
  • 5.3 Quantum Kernel Training
  • 5.4 Pegasos QSVC: Binary Classification
  • 5.5 Quantum Neural Networks
  • 5.5.1 Binary Classification with EstimatorQNN
  • 5.5.2 Classification with a SamplerQNN.
  • 5.5.3 Classification with Variational Quantum Classifier
  • 5.5.4 Regression
  • 5.6 Quantum Generative Adversarial Network
  • 5.7 Quantum Algorithms with HephAIstos
  • References
  • Further Reading
  • Chapter 6 Machine Learning in Production
  • 6.1 Why Use Docker Containers for Machine Learning?
  • 6.1.1 First Things First: The Microservices
  • 6.1.2 Containerization
  • 6.1.3 Docker and Machine Learning: Resolving the "It Works in My Machine" Problem
  • 6.1.4 Quick Install and First Use of Docker
  • 6.1.4.1 Install Docker
  • 6.1.4.2 Using Docker from the Command Line
  • 6.1.5 Dockerfile
  • 6.1.6 Build and Run a Docker Container for Your Machine Learning Model
  • 6.2 Machine Learning Prediction in Real Time Using Docker and Python REST APIs with Flask
  • 6.2.1 Flask-RESTful APIs
  • 6.2.2 Machine Learning Models
  • 6.2.3 Docker Image for the Online Inference
  • 6.2.4 Running Docker Online Inference
  • 6.3 From DevOps to MLOPS: Integrate Machine Learning Models Using Jenkins and Docker
  • 6.3.1 Jenkins Installation
  • 6.3.2 Scenario Implementation
  • 6.4 Machine Learning with Docker and Kubernetes: Install a Cluster from Scratch
  • 6.4.1 Kubernetes Vocabulary
  • 6.4.2 Kubernetes Quick Install
  • 6.4.3 Install a Kubernetes Cluster
  • 6.4.4 Kubernetes: Initialization and Internal Network
  • 6.5 Machine Learning with Docker and Kubernetes: Training Models
  • 6.5.1 Kubernetes Jobs: Model Training and Batch Inference
  • 6.5.2 Create and Prepare the Virtual Machines
  • 6.5.3 Kubeadm Installation
  • 6.5.4 Create a Kubernetes Cluster
  • 6.5.5 Containerize our Python Application that Trains Models
  • 6.5.6 Create Configuration Files for Kubernetes
  • 6.5.7 Commands to Delete the Cluster
  • 6.6 Machine Learning with Docker and Kubernetes: Batch Inference
  • 6.6.1 Create Configuration Files for Kubernetes.
  • 6.7 Machine Learning Prediction in Real Time Using Docker, Python Rest APIs with Flask, and Kubernetes: Online Inference
  • 6.7.1 Flask-RESTful APIs
  • 6.7.2 Machine Learning Models
  • 6.7.3 Docker Image for Online Inference
  • 6.7.4 Running Docker Online Inference
  • 6.7.5 Create and Prepare the Virtual Machines
  • 6.7.6 Kubeadm Installation
  • 6.7.7 Create a Kubernetes Cluster
  • 6.7.8 Deploying the Containerized Machine Learning Model to Kubernetes
  • 6.8 A Machine Learning Application that Deploys to the IBM Cloud Kubernetes Service: Python, Docker, Kubernetes
  • 6.8.1 Create Kubernetes Service on IBM Cloud
  • 6.8.2 Containerization of a Machine Learning Application
  • 6.8.3 Push the Image to the IBM Cloud Registry
  • 6.8.4 Deploy the Application to Kubernetes
  • 6.9 Red Hat OpenShift to Develop and Deploy Enterprise ML/DL Applications
  • 6.9.1 What is OpenShift?
  • 6.9.2 What Is the Difference Between OpenShift and Kubernetes?
  • 6.9.3 Why Red Hat OpenShift for ML/DL? To Build a Production-Ready ML/DL Environment
  • 6.10 Deploying a Machine Learning Model as an API on the Red Hat OpenShift Container Platform: From Source Code in a GitHub Repository with Flask, Scikit-Learn, and Docker
  • 6.10.1 Create an OpenShift Cluster Instance
  • 6.10.1.1 Deploying an Application from Source Code in a GitHub Repository
  • Further Reading
  • Conclusion: The Future of Computing for Data Science?
  • Index
  • EULA.

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